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Nair, B. B., Mohandas, V. P., & Sakthivel, N. R. (2010). A Decision Tree-Rough Set Hybrid System for Stock Market Trend Prediction. International Journal of Computer Applications, 6, 1-6.
https://doi.org/10.5120/1106-1449

has been cited by the following article:

• TITLE: Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market

  AUTHORS: Ya Lin, Rendao Ye

  KEYWORDS: Quantitative Investment, Efficient Market Hypothesis, Machine Learning, Alpha Return

  JOURNAL NAME: Open Journal of Business and Management, Vol.9 No.5, September 16, 2021

  ABSTRACT: With the development of fintech and artificial intelligence, machine learning algorithms are widely used in quantitative investment. Based on the listed companies in China A-share market from February 2005 to July 2020, quantitative stock selection models with machine learning algorithms are established to obtain continuous alpha returns. The results show that machine learning algorithms can effectively identify the relationship between factors and returns and then improve the performance of the quantitative stock selection model. China A-share market is a weak-form efficient market. By mining the factors that are not fully digested by the market, continuous alpha returns can be obtained. The ensemble algorithms represented by the extremely randomized tree (ET) and light gradient boosting machine (LGBM) perform best in stock market prediction.